What is Agent in Artificial Intelligence or AI Agent ?

AI agents are central to modern applications, driving automation and decision-making across industries. They autonomously perceive their environment, process information, make decisions, and take actions to achieve goals. These agents continuously learn to improve their performance. By integrating large language models (LLMs) with tool-calling capabilities, AI agents access real-time data, optimize workflows, and personalize user experiences. For example, in customer support, an AI agent can understand queries, retrieve relevant information, and respond autonomously, boosting efficiency and user engagement.

Core Characteristics of AI Agents

• Autonomy: Operate without human intervention to complete tasks
• Rational decision-making: Use perception and data analysis to choose optimal actions
• Adaptability: Improve performance through memory of past interactions and environmental feedback
• Tool integration: Leverage APIs, databases, and external systems to execute tasks like code generation or customer support

Types of AI agents

AI agents can be developed with different capability levels. A simple agent can be used for simple objectives to keep computational complexity low. From simplest to most complex, there are 5 primary types of agents:

1. Simple reflex agents

Simple reflex agents are the most basic type of agent that decides based on immediate perception. This agent lacks memory, and it does not discuss with other agents if it does not possess the information. These agents operate based on a set of so-called reflexes or rules. This implies that the agent is pre-programmed to execute actions in response to certain conditions being true.

If the agent is faced with a situation for which it has not been pre-programmed, it cannot react in the proper manner. The agents work well in environments that are completely observable with access to all needed information.

Example: Automatic door sensors triggering on motion detection

2. Model-based reflex agents

Model-based reflex agents use both the current perception and memory to keep an internal model of the world. As the agent receives new information constantly, the model is updated. The actions of the agent depend on its model, reflexes, past precepts and current state.

These agents, in contrast to simple reflex agents, can keep information in memory and can work in partially observable and dynamic environments. Nevertheless, they are restricted by their set of rules.6

Example: Autonomous vehicles adjusting routes using real-time sensor data and traffic rules

3. Goal-based agents

Goal-based agents possess an internal model of the world and also possess a goal or a set of goals. These agents look for sequences of actions that bring them to their goal and plan the actions ahead of time before performing them. This planning and search make them more efficient than simple and model-based reflex agents.

Example: Google Maps calculating optimal routes based on traffic/distance

4. Utility-based agents

Utility-based agents choose the sequence of actions that bring them to the goal and maximize utility or reward as well. Utility is computed using a utility function. This function gives a utility value, a measure of the usefulness of an action or how “happy” it will leave the agent, to each situation based on a set of predefined criteria.

The criteria can be things like movement towards the goal, time constraints, or computational complexity. The agent then chooses the actions that maximize the expected utility. These agents are thus helpful in situations where several situations lead to a desired goal and an optimal one must be chosen.7

Example: E-commerce recommendation systems prioritizing user preferences

5. Learning agents

Learning agents possess the same ability of the other types of agents but differ in that they can learn. New experiences are incorporated into their initial knowledge base, which happens automatically. This learning improves the ability of the agent to act in new environments. Learning agents can be utility or goal-based in their reasoning and consist of four primary components:

Learning: This constructs the agent’s knowledge through learning from the world by means of its precepts and sensors.
Critic: This gives feedback to the agent regarding whether the quality of its responses meets the standard of performance.
Performance: This component is tasked with choosing actions after learning.
Problem generator: This generates multiple proposals for actions to be taken.

Example :  Chatbots refining responses based on user interactions

6. Multi-Agent Systems (MAS)

A Multi-Agent System (MAS) is a system composed of multiple autonomous agents that interact within a shared environment to achieve individual or collective goals. These agents, which can be software-based or physical entities, collaborate, negotiate, and sometimes compete to solve complex problems. MAS are used in fields like artificial intelligence, robotics, and distributed computing, offering benefits such as scalability, flexibility, and robustness. They are ideal for applications requiring decentralized control and coordination, including autonomous vehicles, smart grids, and decision-making systems.

Example : Supply chain optimization through collaborative inventory and logistics management

Components of an AI Agent

1. Sensors: Collect data from the environment (e.g., cameras, microphones, APIs).
2. Actuators: Perform actions (e.g., motors, APIs, or output interfaces).
3. Processing Unit: Uses algorithms, rules, or AI models to process data and make decisions.
4. Knowledge Base: Stores information the agent uses to reason and act.
5. Communication Interface: Enables interaction with users or other systems (e.g., chatbots, APIs).

Applications of AI Agents

AI agents are used in a wide range of domains:

1. Virtual Assistants: Siri, Alexa, Google Assistant.
2. Robotics: Autonomous robots in manufacturing, healthcare, and exploration.
3. Gaming: Non-player characters (NPCs) in video games.
4. Finance: Algorithmic trading systems.
5. Healthcare: Diagnostic tools and personalized treatment recommendations.
6. Customer Support: Chatbots for answering queries and resolving issues.
7. Smart Homes: Devices like smart thermostats and security systems.

How AI Agents Work

1. Perceive: Gather data from the environment or user input.
2. Process: Analyze the data using AI models, rules, or algorithms.
3. Decide: Determine the best action based on the analysis.
4. Act: Execute the action to achieve the goal.
5. Learn (Optional): Improve performance over time using feedback or new data.

AI Agents Examples

1. Chatbots: Use natural language processing (NLP) to interact with users and provide information.
2. Self-Driving Cars: Use sensors and AI to navigate and make driving decisions.
3. Recommendation Systems: Analyze user behavior to suggest products, movies, or content.
4. Smart Home Devices: Control lights, temperature, and security based on user preferences.

Comparison: Agentic vs. Non-Agentic AI Chatbots

AI agents are often compared with traditional chatbots. Here’s how they differ:

AI Agents vs. Traditional Software

• Traditional Software: Follows predefined instructions and rules without adaptability.
• AI Agents: Can learn, adapt, and make decisions based on dynamic inputs and environments.

Conclusion

AI agents represent the next stage of AI evolution, enabling advanced decision-making, automation, and personalization. As AI continues to evolve, we can expect:

• More autonomous AI systems with enhanced reasoning capabilities.
• Improved human-AI collaboration through intelligent virtual assistants.
• Scalable applications across industries such as healthcare, finance, and education.

With continuous advancements in AI, AI agents will play a pivotal role in shaping the future of intelligent systems and digital transformation.